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Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles with High Penetration of Behind-The-Meter Solar

Journal Article · · IEEE Transactions on Industry Applications
 [1];  [2];  [1];  [1];  [3]
  1. Temple Univ., Philadelphia, PA (United States)
  2. PJM Interconnection, Audobon, PA (United States
  3. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
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The ever-growing integration of distributed energy resources (DERs), especially behind-the-meter (BTM) solar generations, poses imperative operational challenges to system operators such as regional transmission organizations (RTOs). It is important for RTOs to effectively and accurately extract actual load profiles at the transmission level for a single node with significant BTM solar injection. This paper first illustrates the necessity of disaggregating the daily actual load profile of a single node. Furthermore, by segmenting nodes with selected timeseries features, nodes with significant BTM solar generation are identified. Lastly, a bi-level framework is proposed, comprising reference node disaggregation and DeepFM nodal disaggregation, aimed at disaggregating the nodal load profiles from which system operators require more information. By adopting a hybrid Deep Factorization Machine (DeepFM) model, the model achieve accurate results by extracting both linear and nonlinear relations between nodes in the same region and the zonal load and nodal load profile. To overcome the lack of ground truth, this paper segments the load profile into daytime, nighttime, and zero-crossing points and utilizes the latter two for evaluation purposes. The proposed disaggregation procedure is validated using real world, minute-level, normalized, and anonymized nodal data in the PJM service territory.
Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
2574049
Report Number(s):
PNNL-SA--206721
Journal Information:
IEEE Transactions on Industry Applications, Journal Name: IEEE Transactions on Industry Applications Journal Issue: 2 Vol. 61; ISSN 0093-9994
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

Figures / Tables (9)

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Related Subjects

42 ENGINEERING
Behind-the-meter Solar
Factorization Machine
Load Disaggregation